{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6bd817c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Affine\n",
    "def affine(img,a,b,c,d,tx,ty):\n",
    "\tH,W,C=img.shape\n",
    "\n",
    "\t#temporaryimage\n",
    "\timg=np.zeros((H+2,W+2,C),dtype=np.float32)\n",
    "\timg[1:H+1,1:W+1]=_img\n",
    "\n",
    "\t#getnewimageshape\n",
    "\tH_new=np.round(H*d).astype(np.int)\n",
    "\tW_new=np.round(W*a).astype(np.int)\n",
    "\tout=np.zeros((H_new+1,W_new+1,C),dtype=np.float32)\n",
    "\t#getpositionofnewimage\n",
    "\tx_new=np.tile(np.arange(W_new),(H_new,1))\n",
    "\ty_new=np.arange(H_new).repeat(W_new).reshape(H_new,-1)\n",
    "\n",
    "\t#getpositionoforiginalimagebyaffine\n",
    "\tadbc=a*d-b*c\n",
    "\tx=np.round((d*x_new-b*y_new)/adbc).astype(np.int)-tx+1\n",
    "\ty=np.round((-c*x_new+a*y_new)/adbc).astype(np.int)-ty+1\n",
    "\n",
    "\tx=np.minimum(np.maximum(x,0),W+1).astype(np.int)\n",
    "\ty=np.minimum(np.maximum(y,0),H+1).astype(np.int)\n",
    "\n",
    "\t#assginpixceltonewimage\n",
    "\tout[y_new,x_new]=img[y,x]\n",
    "\n",
    "\tout=out[:H_new,:W_new]\n",
    "\tout=out.astype(np.uint8)\n",
    "\n",
    "\treturn out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39c6648c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "216cc9d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# _img = cv2.imread(\"imori.jpg\").astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "007b22e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "054f7216",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\hongpu_workspace\\hongpu_wheel\\env\\lib\\site-packages\\ipykernel_launcher.py:10: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "d:\\hongpu_workspace\\hongpu_wheel\\env\\lib\\site-packages\\ipykernel_launcher.py:11: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "d:\\hongpu_workspace\\hongpu_wheel\\env\\lib\\site-packages\\ipykernel_launcher.py:19: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "d:\\hongpu_workspace\\hongpu_wheel\\env\\lib\\site-packages\\ipykernel_launcher.py:20: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "d:\\hongpu_workspace\\hongpu_wheel\\env\\lib\\site-packages\\ipykernel_launcher.py:22: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "d:\\hongpu_workspace\\hongpu_wheel\\env\\lib\\site-packages\\ipykernel_launcher.py:23: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16c70238b48>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Read image\n",
    "_img = cv2.imread(\"imori.jpg\").astype(np.int32)\n",
    "# # Affine\n",
    "out = affine(_img, a=1, b=0, c=0, d=1, tx=50, ty=100)\n",
    "plt.imshow(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e86aff75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5d477d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
